Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
import pickle
def load_data_from_file(filePath, colnames):
with open(filePath, mode='rb') as f:
file = pickle.load(f)
rtn = tuple(map(lambda x:file[x],colnames))
return rtn
In [3]:
mtx, dist = load_data_from_file('./camera_calibration.p',['mtx','dist'])
In [4]:
def abs_sobel_thresh(gray, orient='x', sobel_kernel=3, thresh=(0, 255)):
sobel = cv2.Sobel(gray,cv2.CV_64F,orient=='x',orient=='y',ksize=sobel_kernel)
abs_sobel = np.absolute(sobel)
scaled_sobel = np.uint8(255 * abs_sobel / np.max(abs_sobel))
sobel_binary = np.zeros_like(scaled_sobel)
sobel_binary[(scaled_sobel > thresh[0]) & (scaled_sobel <= thresh[1])] = 1
return sobel_binary
def mag_sobel_thresh(gray, sobel_kernel=3, thresh=(0, 255)):
sobelx = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=sobel_kernel)
sobely = cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=sobel_kernel)
mag_sobel = np.sqrt(np.square(sobelx) + np.square(sobely))
scaled_sobel = np.uint8(255*mag_sobel/np.max(mag_sobel))
mag_binary = np.zeros_like(scaled_sobel)
mag_binary[(scaled_sobel > thresh[0]) & (scaled_sobel <= thresh[1])] = 1
return mag_binary
def dir_sobel_threshold(gray, sobel_kernel=3, thresh=(0, np.pi/2)):
sobelx = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=sobel_kernel)
sobely = cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=sobel_kernel)
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
dir = np.arctan2(abs_sobely,abs_sobelx)
dir_binary = np.zeros_like(dir)
dir_binary[(dir > thresh[0]) & (dir <= thresh[1])] = 1
return dir_binary
In [5]:
def thresh_image(img, thresh=(20, 100)):
binary = np.zeros_like(img)
binary[((img > thresh[0]) & (img <= thresh[1]))] = 1
return binary
In [6]:
def get_region_of_interest():
bottomY = 720
topY = 455
left1 = (190, bottomY)
left2 = (585, topY)
right1 = (705, topY)
right2 = (1130, bottomY)
return np.float32((left2,right1,right2,left1))
In [7]:
def get_transform_dest(w,h):
offset = 250
dst = np.float32([[offset, 0],[w-offset, 0],[w-offset, h], [offset, h]])
return dst
In [8]:
def get_transform_matrix(src_pts,dest_pts):
M = cv2.getPerspectiveTransform(src_pts, dest_pts)
return M
In [9]:
def perspective_transform(img, M):
w,h = img.shape[1::-1]
transed = cv2.warpPerspective(img,M,(w,h),flags=cv2.INTER_LINEAR)
return transed
In [25]:
def add_region_of_interest_to_image(src_image,pts):
left2 = pts[0]
right1 = pts[1]
right2 = pts[2]
left1 = pts[3]
color = [255, 0, 0]
w = 2
output = src_image.copy()
cv2.line(output, tuple(left1), tuple(left2), color, w)
cv2.line(output, tuple(left2), tuple(right1), color, w)
cv2.line(output, tuple(right1), tuple(right2), color, w)
cv2.line(output, tuple(right2), tuple(left1), color, w)
return output
In [10]:
import matplotlib.image as mpimg
sample_image = mpimg.imread('./test_images/straight_lines2.jpg')
w,h = sample_image.shape[1::-1]
src = get_region_of_interest()
dst = get_transform_dest(w,h)
M = get_transform_matrix(src,dst)
inverseM = get_transform_matrix(dst,src)
import pickle
pickle.dump( { 'M': M, 'inverseM': inverseM }, open('./transform.p', 'wb'))
In [11]:
print(M)
print(inverseM)
In [12]:
import matplotlib.image as mpimg
def pipeline_single_image(rgbimage,ksize=3, s_thresh=(80, 250), sobelx_thresh=(20, 100)):
# Undistort
undist_img = cv2.undistort(rgbimage, mtx, dist, None, mtx)
gray = cv2.cvtColor(undist_img,cv2.COLOR_RGB2GRAY)
hls = cv2.cvtColor(undist_img, cv2.COLOR_RGB2HLS).astype(np.float)
h_channel = hls[:, :, 0]
# l_channel = hls[:, :, 1]
s_channel = hls[:, :, 2]
s_binary = thresh_image(s_channel,thresh=s_thresh)
sx_binary = abs_sobel_thresh(gray, orient='x', sobel_kernel=ksize, thresh=sobelx_thresh)
h_binary = thresh_image(h_channel,thresh=(5,100))
dir_binary = dir_sobel_threshold(gray,sobel_kernel=ksize,thresh=(0.7,1.3))
mag_binary = mag_sobel_thresh(gray, sobel_kernel=ksize, thresh=(30, 100))
sh_binary = np.zeros_like(gray)
sh_binary[(s_binary == 1) & (h_binary == 1)] = 1
combined_binary = np.zeros_like(gray)
# combined_binary[(s_binary == 1) | (sx_binary == 1)] = 1
combined_binary[(sh_binary == 1) | (sx_binary == 1)] = 1
# combined_binary[((s_binary == 1) & (sx_binary == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
# perspective transform
w,h = rgbimage.shape[1::-1]
src = get_region_of_interest()
dst = get_transform_dest(w,h)
M = get_transform_matrix(src,dst)
trans_binary = perspective_transform(combined_binary,M)
return trans_binary,combined_binary,s_binary,h_binary,sh_binary,sx_binary
In [22]:
import matplotlib.image as mpimg
def pipeline_image_paths_set(image_files_set,ksize=3, s_thresh=(80, 250), sobelx_thresh=(20, 100)):
image_set = []
trans_set = []
combined_set = []
shinary_set = []
sxbinary_set = []
for image_file_path in image_files_set:
rgbimg = mpimg.imread(image_file_path)
tr_bin,comb_bin,s_bin,h_bin,sh_bin,sx_bin = pipeline_single_image(rgbimg,ksize,s_thresh,sobelx_thresh)
image_set.append(rgbimg)
trans_set.append(tr_bin)
combined_set.append(comb_bin)
shinary_set.append(sh_bin)
sxbinary_set.append(sx_bin)
return list(zip(image_set,trans_set,combined_set,shinary_set,sxbinary_set))
In [23]:
def show_image_set(image_files, dataset_zip, titles, col_per_row = 5):
for file_name,item in zip(image_files,dataset_zip):
img,tr_bin,c_bin,s_bin,sx_bin = item
fig = plt.figure(figsize = (32, 12))
fig.subplots_adjust(left = 0, right = 1, bottom = 0, top = 1, hspace = 0.01, wspace = 0.01)
pts = get_region_of_interest()
img = add_region_of_interest_to_image(img,pts)
plt.subplot(1,col_per_row,1)
plt.imshow(img)
plt.title(file_name,fontsize=20)
plt.axis('off')
plt.subplot(1,col_per_row,2)
plt.imshow(tr_bin,cmap='gray')
plt.title(titles[0],fontsize=20)
plt.axis('off')
plt.subplot(1,col_per_row,3)
plt.imshow(c_bin,cmap='gray')
plt.title(titles[1],fontsize=20)
plt.axis('off')
plt.subplot(1,col_per_row,4)
plt.imshow(s_bin,cmap='gray')
plt.title(titles[2],fontsize=20)
plt.axis('off')
plt.subplot(1,col_per_row,5)
plt.imshow(sx_bin,cmap='gray')
plt.title(titles[3],fontsize=20)
plt.axis('off')
In [13]:
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30 / 720 # meters per pixel in y dimension
xm_per_pix = 3.7 / 700 # meters per pixel in x dimension
In [26]:
import glob
titles = ['Transform','Combined','S + H','Sobel X']
image_files_names = glob.glob('test_images/*.jpg')
# image_files_names = glob.glob('harder_test/0-*.jpg')
dataset_zip = pipeline_image_paths_set(image_files_names,ksize=3, s_thresh=(80, 250), sobelx_thresh=(30, 150))
image_set = []
trans_set = []
combined_set = []
sbinary_set = []
sxbinary_set = []
for img,tr_bin,c_bin,s_bin,sx_bin in dataset_zip:
image_set.append(img)
trans_set.append(tr_bin)
combined_set.append(c_bin)
sbinary_set.append(s_bin)
sxbinary_set.append(sx_bin)
# print(len(trans_set))
show_image_set(image_files_names,dataset_zip,titles)
# show_images(trans_set,image_files_names,cmap='gray')
# for ind in range(len(combined_set)):
# mpimg.imsave('./{}.jpg'.format(ind+1),combined_set[ind])
# test1_set = combined_set
# index = 0
# showi = test1_set[index]*255
# mpimg.imsave('./1.jpg',test1_set[])
# mpimg.imsave('./2.jpg',test1_set[5])
# mpimg.imsave('./3.jpg',showi)
# mpimg.imsave('./4.jpg',test1_set[7])
# w,h = showi.shape[1::-1]
# src = get_region_of_interest()
# dst = get_transform_dest(w,h)
# M = get_transform_matrix(src,dst)
# showi_trans = perspective_transform(showi,M)
# plt.imshow(showi,cmap='gray')
In [27]:
# show_images(combined_set)
# print(image_files_names)
imagefit_set = find_and_show_lane_lines_rgb_image_set(image_set,image_files_names,cols=4,rows=3,figsize=(24,12))
In [14]:
def find_lane_on_single_rgb_image(rgbimage, nwindows=9, margin=110, minpix=50, left_fit=None, right_fit=None):
binary_warped,_,_,_,_,_ = pipeline_single_image(rgbimage,ksize=3, s_thresh=(80, 250), sobelx_thresh=(30, 150))
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
if (left_fit is None) | (right_fit is None):
left_lane_inds, right_lane_inds = sliding_window_search(binary_warped,out_img,nonzerox,nonzeroy,nwindows,margin,minpix)
else:
left_lane_inds = ((nonzerox > (left_fit[0] * (nonzeroy ** 2) + left_fit[1] * nonzeroy + left_fit[2] - margin))
& (nonzerox < (left_fit[0] * (nonzeroy ** 2) + left_fit[1] * nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0] * (nonzeroy ** 2) + right_fit[1] * nonzeroy + right_fit[2] - margin))
& (nonzerox < (right_fit[0] * (nonzeroy ** 2) + right_fit[1] * nonzeroy + right_fit[2] + margin)))
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Fit a second order polynomial to each
left_fit_m = np.polyfit(lefty * ym_per_pix, leftx * xm_per_pix, 2)
right_fit_m = np.polyfit(righty * ym_per_pix, rightx * xm_per_pix, 2)
return (left_fit, right_fit, left_fit_m, right_fit_m, left_lane_inds, right_lane_inds, out_img, nonzerox, nonzeroy)
def visualize_lane_lines_single_rgb_image(rgbimage,ax,lfit=None,rfit=None):
left_fit, right_fit, left_fit_m, right_fit_m, left_lane_inds, right_lane_inds, out_img, nonzerox, nonzeroy = find_lane_on_single_rgb_image(rgbimage,left_fit=lfit,right_fit=rfit)
# Visualization
ploty = np.linspace(0, rgbimage.shape[0]-1, rgbimage.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
margin = 80
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
window_img = np.zeros_like(out_img)
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
ax.imshow(result)
ax.plot(left_fitx, ploty, color='yellow')
ax.plot(right_fitx, ploty, color='yellow')
return ( left_fit, right_fit, left_fit_m, right_fit_m)
def find_and_show_lane_lines_rgb_image_set(rgbimage_set,file_path_set,cols = 2, rows = 3, figsize=(15,13)):
nums = len(rgbimage_set)
fig, axes = plt.subplots(rows, cols, figsize=figsize)
plt.axis('off')
indexes = range(cols * rows)
imagefit_set = []
lfit = None
rfit = None
for ax, index in zip(axes.flat, indexes):
if index < nums:
rgbimage = rgbimage_set[index]
path = file_path_set[index]
left_fit, right_fit, left_fit_m, right_fit_m = visualize_lane_lines_single_rgb_image(rgbimage, ax,lfit=lfit,rfit=rfit)
lfit = left_fit
rfit = right_fit
ax.set_title(path)
ax.axis('off')
imagefit_set.append( ( path, left_fit, right_fit, left_fit_m, right_fit_m ) )
return imagefit_set
In [15]:
def sliding_window_search(binary_warped,out_img,nonzerox,nonzeroy,nwindows=9, margin=110, minpix=50):
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0] / 2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set height of windows
window_height = np.int(binary_warped.shape[0] // nwindows)
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window + 1) * window_height
win_y_high = binary_warped.shape[0] - window * window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low), (win_xright_high, win_y_high), (0, 255, 0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (
nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (
nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
return (left_lane_inds, right_lane_inds)
In [16]:
def calculate_curvature(yRange, left_fit_cr):
return ((1 + (2*left_fit_cr[0]*yRange*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
In [28]:
for imgfit in imagefit_set:
path, left_fit, right_fit, left_fit_m, right_fit_m = imgfit
leftCurvature = calculate_curvature(719, left_fit_m) / 1000
rightCurvature = calculate_curvature(719, right_fit_m) / 1000
print('{}, Left - {:.2f} km, Right - {:.2f} km'.format(path, leftCurvature, rightCurvature))
In [32]:
lanes_set = []
for image_fit,img in zip(imagefit_set,image_set):
_, left_fit, right_fit, left_fit_m, right_fit_m = image_fit
image_with_lane = draw_lane_area_on_image(img,left_fit,right_fit)
image_with_lane = draw_lane_info_on_image(image_with_lane,left_fit_m,right_fit_m)
lanes_set.append(image_with_lane)
show_images(lanes_set,image_files_names)
In [33]:
for org,lane,name in zip(image_set,lanes_set,image_files_names):
show_2_images_side_by_side(org,lane,name,'',cmap1=None, cmap2=None)
In [20]:
def draw_lane_area_on_image(img,l_fit,r_fit):
yMax = img.shape[0]
ploty = np.linspace(0, yMax - 1, yMax)
color_warp = np.zeros_like(img).astype(np.uint8)
# Calculate points.
left_fitx = l_fit[0]*ploty**2 + l_fit[1]*ploty + l_fit[2]
right_fitx = r_fit[0]*ploty**2 + r_fit[1]*ploty + r_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (inverseM)
newwarp = cv2.warpPerspective(color_warp, inverseM, (img.shape[1], img.shape[0]))
output = cv2.addWeighted(img, 1, newwarp, 0.3, 0)
return output
def draw_lane_info_on_image(img, l_fit_m, r_fit_m,fontScale=2):
output = img.copy()
# Calculate curvature
leftCurvature = calculate_curvature(719, l_fit_m)
rightCurvature = calculate_curvature(719, r_fit_m)
# Calculate vehicle center
xMax = img.shape[1]*xm_per_pix
yMax = img.shape[0]*ym_per_pix
middle = xMax / 2
lineLeft = l_fit_m[0]*yMax**2 + l_fit_m[1]*yMax + l_fit_m[2]
lineRight = r_fit_m[0]*yMax**2 + r_fit_m[1]*yMax + r_fit_m[2]
lineMiddle = lineLeft + (lineRight - lineLeft)/2
diff = lineMiddle - middle
if diff > 0.01:
message = '{:.2f} m right of lane center'.format(diff)
elif diff < -0.01:
message = '{:.2f} m left of lane center'.format(-diff)
else:
message = 'At center of the lane'
# Draw info
font = cv2.FONT_HERSHEY_SIMPLEX
fontColor = (255, 255, 255)
cv2.putText(output, 'Left: {:.1f} m / Right: {:.1f} m'.format(leftCurvature,rightCurvature), (50, 50), font, fontScale, fontColor, 2)
cv2.putText(output, message, (50, 120), font, fontScale, fontColor, 2)
return output
In [30]:
def show_images(image_set, title_set=None,counts=None,cmap=None):
num_image_toshow = len(image_set) if counts is None else counts
plt.figure(1, figsize=(20, 36))
for idx in range(num_image_toshow):
plt.subplot(9,4, idx + 1)
img = image_set[idx]
if img.shape[0] >0:
if cmap is 'gray':
plt.imshow(img,cmap='gray')
else:
plt.imshow(img)
if title_set is not None:
s = '{}'.format(title_set[idx])
plt.title(s)
plt.axis('off')
plt.tight_layout()
In [31]:
def show_2_images_side_by_side(img1, img2, title1='',title2='',cmap1=None,cmap2='gray'):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
if cmap1 == None:
ax1.imshow(img1)
else:
ax1.imshow(img1,cmap=cmap1)
ax1.set_title(title1, fontsize=30)
ax1.axis('off')
if cmap2 == None:
ax2.imshow(img2)
else:
ax2.imshow(img2, cmap=cmap2)
ax2.set_title(title2, fontsize=30)
ax2.axis('off')
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Video Processing¶
In [21]:
from moviepy.editor import VideoFileClip
class Lane():
def __init__(self):
self.left_fit = None
self.right_fit = None
self.left_fit_m = None
self.right_fit_m = None
self.left_curvature = None
self.right_curvature = None
def find_lanes_and_calculate_curvatures(img,l_fit=None,r_fit=None):
left_fit, right_fit, left_fit_m, right_fit_m, _, _, _, _, _ = find_lane_on_single_rgb_image(img,margin=110,left_fit=l_fit,right_fit=r_fit)
# Calculate curvature
left_curvature = calculate_curvature(719, left_fit_m)
right_curvature = calculate_curvature(719, right_fit_m)
# Calculate vehicle center
xMax = img.shape[1]*xm_per_pix
yMax = img.shape[0]*ym_per_pix
vehicleCenter = xMax / 2
lineLeft = left_fit_m[0]*yMax**2 + left_fit_m[1]*yMax + left_fit_m[2]
lineRight = right_fit_m[0]*yMax**2 + right_fit_m[1]*yMax + right_fit_m[2]
lineMiddle = lineLeft + (lineRight - lineLeft)/2
diff = lineMiddle - vehicleCenter
return (left_fit, right_fit, left_fit_m, right_fit_m, left_curvature, right_curvature, diff)
def visualize_lanes(img, left_fit, right_fit, left_fit_m, right_fit_m, left_curvature, right_curvature, diff):
output = draw_lane_area_on_image(img, left_fit, right_fit)
if diff > 0.01:
message = '{:.2f} m right of lane center'.format(diff)
elif diff < -0.01:
message = '{:.2f} m left of lane center'.format(-diff)
else:
message = 'At center of the lane'
# Draw info
font = cv2.FONT_HERSHEY_SIMPLEX
fontColor = (255, 255, 255)
cv2.putText(output, 'Left: {:.1f} m / Right: {:.1f} m'.format(left_curvature, right_curvature), (50, 50), font,
1, fontColor, 2)
cv2.putText(output, message, (50, 120), font, 1, fontColor, 2)
return output
def video_pipeline(input, output):
myclip = VideoFileClip(input)
leftLane = Lane()
rightLane = Lane()
def processImage(img):
new_left_fit, new_right_fit, left_fit_m, right_fit_m, left_curvature, right_curvature, diff = find_lanes_and_calculate_curvatures(img,l_fit=leftLane.left_fit,r_fit=rightLane.right_fit)
leftLane.left_fit = new_left_fit
leftLane.left_fit_m = left_fit_m
leftLane.left_curvature = left_curvature
rightLane.right_fit = new_right_fit
rightLane.right_fit_m = right_fit_m
rightLane.right_curvature = right_curvature
return visualize_lanes(img, new_left_fit, new_right_fit, left_fit_m, right_fit_m, left_curvature, right_curvature, diff)
clip = myclip.fl_image(processImage)
clip.write_videofile(output, audio=False)
# Project video
video_pipeline('project_video.mp4', 'out_project_video.mp4')
In [35]:
from IPython.display import HTML
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format('out_project_video.mp4'))
Out[35]: